Citizen Science with GIS&amp;T

Rickles, Patrick; Haklay, Muki; Ellul, C; Skarlatidou, Artemis

doi:10.22224/gistbok/2017.3.5

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…(Kerski 2016) GS-24: Discuss different types of Citizen Science activities (passive sensing, volunteer computing, volunteer thinking, environmental and ecological observation, participatory sensing, community/civic science) and potential motivations (intrinsic and extrinsic motives) participants may have to engage with projects. (Rickles et al 2017) 7. understand how the commodification of their personal information and online interactions affects the information they receive and the information they produce or disseminate online DA-24: Describe how big data, continuous data tracking and streaming, the Internet of things (IoT) and SoLoMo marketing have enhanced enterprise knowledge of current and potential customers. Explain how location analytics tools have contributed to these technologies.…”

Section: Acrl Framework Gisandt Body Of Knowledge a Knowledge Practicesmentioning

confidence: 99%

Mapping Concepts to Locate Learning Opportunities: Aligning the ACRL Framework to the GIS&T Body of Knowledge

Sadvari

2019

Journal of Map & Geography Libraries

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The adoption of the Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education in 2016 coincided with the launch of the digital edition of the Geographic Information Science and Technology (GIS&T) Body of Knowledge. The GIS&T Body of Knowledge and the ACRL Framework share a common goal of providing a flexible, communitydriven, living document to support teaching and learning in higher education. The Body of Knowledge serves as a representation of the GIS&T knowledge domain, while the ACRL Framework aids librarians in integrating core information literacy concepts into instruction in their respective knowledge domains. Despite this connection, no attempt to evaluate how these guiding documents can be aligned to one another to inform instructional practice has yet been reported. This study uses a relative crosswalk approach to map connections between the knowledge practices and dispositions in the ACRL Framework and the learning objectives in the GIS&T Body of Knowledge. This analysis highlights alignment between these documents and can serve as a conceptual foundation to make it easier for map, geospatial, and subject librarians to identify practical opportunities for integrating information literacy instruction into GIS&T education, both independently and in collaboration with disciplinary faculty.

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Section: Acrl Framework Gisandt Body Of Knowledge a Knowledge Practicesmentioning

confidence: 99%

Mapping Concepts to Locate Learning Opportunities: Aligning the ACRL Framework to the GIS&T Body of Knowledge

Sadvari

2019

Journal of Map & Geography Libraries

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“…In fact, collegial groups can form a core team that organizes activities and crowdsources data collection from the general public. The collegial participation model is a subset of citizen science that might particularly want institutional recognition of their work [15] and, as a result, are more likely to produce documentation of their project and even publish academically [9].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Hultquist

Oravecz

Cervone

2021

IJGI

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Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making.

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Citizen Science with GIS&T

Cited by 2 publications

References 33 publications

Mapping Concepts to Locate Learning Opportunities: Aligning the ACRL Framework to the GIS&T Body of Knowledge

Mapping Concepts to Locate Learning Opportunities: Aligning the ACRL Framework to the GIS&T Body of Knowledge

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

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